Tsallis Entropy In Bi-level And Multi-level Image Thresholding

Tsallis Entropy In Bi-level And Multi-level Image Thresholding

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Author(s)

Author(s): Amelia Carolina Sparavigna

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DOI: 10.18483/ijSci.613 511 1179 40-49 Volume 4 - Jan 2015

Abstract

The maximum entropy principle has a relevant role in image processing, in particular for thresholding and image segmentation. Different entropic formulations are available to this purpose; one of them is based on the Tsallis non-extensive entropy. Here, we propose a discussion of its use for bi- and multi-level thresholding.

Keywords

Image Processing, Tsallis Entropy, Thresholding

References

  1. Tsallis, C. (1988). Possible Generalization of Boltzmann-Gibbs Statistics. Journal of Statistical Physics, 52, 479-487
  2. Tsallis, C. (2009). Nonadditive Entropy and Nonextensive Statistical Mechanics - an Overview after 20 Years. Braz. J. Phys, 39, 337-35
  3. Djerou, L., Khelil, N., Dehimi, N.H., & Batouche, M. (2012). Automatic Multi-Level Thresholding Segmentation Based on Multi-Objective Optimization. Journal of Applied Computer Science & Mathematics, 13 (6), 24-31
  4. Renyi, A. (1970). Probability Theory. North-Holland, Amsterdam
  5. Maszczyk, T., & Duch, W. (2008). Comparison of Shannon, Renyi and Tsallis Entropy Used in Decision Trees. Artificial Intelligence and Soft Computing - ICAISC 2008, 643-651, Springer Berlin Heidelberg
  6. Havrda, J., & Charvát, F., (1967). Quantification Methods of Classification Processes: Concept of Structural Alpha-Entropy. Kybernetica (Prague) 3, 95-100
  7. Sahoo, P.K., & Arora, G. (2006). Image Thresholding Using Two-Dimensional Tsallis– Havrda– Charvát. Pattern Recognition Letters, 27, 520-528
  8. Gull, S.F., & Skilling, J. (1984). Maximum Entropy Method in Image Processing. Communications, Radar and Signal Processing, IEE Proceedings F, 131(6), 646-659
  9. Yamano, T. (2001). Information Theory Based in Nonadditive Information Content. Entropy 3, 280–292; Yamano, T. (2000). arXiv:cond-mat/0010074 [cond-mat.stat-mech]
  10. Portes de Albuquerque, M., Esquef, I.A., Gesualdi Mello, A.R., & Portes de Albuquerque, M. (2004). Image Thresholding Using Tsallis Entropy. Pattern Recognition Letters, 25(9), 1059-1065
  11. Sezgin, M., & Sankur, B. (2004). Survey over Image Thresholding Techniques and Quantitative Performance Evaluation. Journal of Electronic Imaging, 13(1), 146-165
  12. Kapur, J.N., Sahoo, P.K., & Wong, A.K.C. (1985). A New Method for Gray-Level Picture Thresholding Using the Entropy of the Histogram. Comput. Vision Graphics Image Process., 29, 273-285
  13. Shitong Wang, & Chung, F.L. (2005). Note on the Equivalence Relationship between Renyi-Entropy Based and Tsallis-Entropy Based Image Thresholding. Pattern Recognition Letters, 26(14), 2309-2312
  14. Sahoo, P., Wilkins, C., & Yeager, J. (1997). Thresholding Selection Using Renyi’s Entropy. Pattern Recognition, 30 (1), 71–84
  15. Sahoo, P.K., & Arora, G. (2004). A Thresholding Method Based on Two Dimensional Renyi’s Entropy. Pattern Recognition, 37 (6), 1149-1161
  16. Sparavigna, A. (2009). The Digital Restoration of Da Vinci's Sketches. arXiv:0903.1448 [cs.CV]
  17. Sparavigna, A.C. (2011). An Image Processing of a Raphael's Portrait of Leonardo. arXiv:1111.6030 [cs.CV]
  18. Sparavigna, A.C. (2011). Portraits of Leonardo da Vinci. Archaeogate, 6 December 1011
  19. Sathya, P. D., & Kayalvizhi, R. (2010). PSO-based Tsallis Thresholding Selection Procedure for Image Segmentation. International Journal of Computer Applications, 5(4), 39-46
  20. Yudong Zhang, & Lenan Wu, Optimal Multi-Level Thresholding Based on Maximum Tsallis Entropy via an Artificial Bee Colony Approach. Entropy 2011, 13(4), 841-859
  21. Tamalika Chaira (2015). Medical Image Processing: Advanced Fuzzy Set Theoretic Techniques. CRC Press
  22. Shi Weili, Miao Yu, Chen Zhanfang, & Zhang Hongbiao (2009). Research of Automatic Medical Image Segmentation Algorithm Based on Tsallis Entropy and Improved PCNN. International Conference on Mechatronics and Automation, ICMA 9-12 August 2009, 1004-1008
  23. Yudong Zhang, & Lenan Wu (2008). Pattern Recognition via PCNN and Tsallis Entropy. Sensors, 8(11), 7518-7529
  24. El-Sayed, M.A., Abdel-Khalek, S. & Abdel-Aziz, E. (2011). Study of Efficient Technique Based on 2D Tsallis Entropy for Image Thresholding. International Journal on Computer Science and Engineering (IJCSE), 3(9), 3125-3138
  25. Ramirez-Villegas, J.F., & Ramirez-Moreno, D.F. (2012). Wavelet Packet Energy, Tsallis Entropy and Statistical Parameterization for Support Vector-Based and Neural-Based Classification of Mammographic Regions. Neurocomputing, 77(1), 82–100
  26. El-Sayed, M.A. (2011). A New Algorithm Based Entropic Threshold for Edge Detection in Images. IJCSI - International Journal of Computer Science Issues, 8(5), 71-78
  27. Khader, M. & Ben Hamza, A. (2012). An Information- Theoretic Method for Multimodality Medical Image Registration. Expert Systems with Applications, 39(5), 5548-5556
  28. Fabbri, R., Gonçalves, W.N., Lopes, F.J.P., & Bruno, O.M. (2012). Multi-q Pattern Analysis: A Case Study in Image Classification, Physica A: Statistical Mechanics and its Applications, 391(19), 4487-4496

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International Journal of Sciences is Open Access Journal.
This article is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License.
Author(s) retain the copyrights of this article, though, publication rights are with Alkhaer Publications.

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